Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19

Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19

The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation mo...

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Veröffentlicht in:arXiv.org 2020-09
Hauptverfasser: Wang, Ding, Zuo, Fan, Gao, Jingqin, He, Yueshuai, Bian, Zilin, Suzana Duran Bernardes, Chaekuk Na, Wang, Jingxing, Petinos, John, Ozbay, Kaan, Chow, Joseph Y J, Iyer, Shri, Nassif, Hani, Xuegang Jeff Ban
Format: Artikel
Sprache:eng
Schlagworte:
Agent-based models
Coronaviruses
COVID-19
Deep learning
Disease control
Image processing
Social distancing
Transportation systems
Trends
Video
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Beschreibung
Zusammenfassung:The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing. This edition of the white paper updates travel trends and highlights an agent-based simulation model's results to predict the impact of proposed phased reopening strategies. It also introduces a real-time video processing method to measure social distancing through cameras on city streets.
ISSN:2331-8422